I think you're misunderstanding the paradigm shift completely -- AI does not just generate code N(x) more quickly. It thinks N(x) faster, it researches N(x) faster, it tests N(x) faster. There are hundreds of tasks that you'll find engineers are offloading to AI every day. The major hurdle right now is actually pivoting LLMs from just generating code: integrating those tasks into workflows. This is why tool-use and agentic workflows have taken engineering by storm.
Debugging, sanity checking, testing, etc. are the best uses of LLMs. Much better than writing code.
Developers should write their own code and use LLMs to design and verify. Better, faster architecture and planning, pre-cleaned PRs and no skill atrophy or loss of understanding on the part of the developer.
Not sure why you are downvoted but I agree. Additionally, perhaps LLMs are just like another higher programming language as the author said, and they still need someone to steer them.
I'm sure it was very difficult to program in machine code, but if now (or soon) anyone can just write software using a LLM without any sort of learning it changes everything. LLMs can plan and create something usable from simple instructions or ideas, and they will only get better.
I think LLMs will be (and already are) useful for many more things than programming anyway.
> I'm sure it was very difficult to program in machine code, but if now (or soon) anyone can just write software using a LLM without any sort of learning it changes everything. LLMs can plan and create something usable from simple instructions or ideas, and they will only get better.
Did you read the section "Power to the People?" ? In it, the author dismantles your thesis with powerful, highly plausible arguments.
I don't buy that's true. The "only" part, anyway. Look at how UX with software has evolved. This is gonna be an old man yells at clouds take, but before smartphones, there were hotkeys. And man, you could fly with those things. The computers running things weren't as fast as they are today, but you could mash in a a whole sequence thru muscle memory, and just wait for it to complete. Now, you have to poke at your phone, wait for it to respond, poke at it some more. It's really not great for getting fast at it. AI advancement is going to be like that. Directionally generally it will be better, but there's going to be some niche where, y'know what, ChatGPT-4o really had it in a way that 5.5 does not. (Rose colored glasses not included.)
Claude connected to a Postgres (readonly obviously) and Datadog MCP servers in addition to access to the codebase can debug prod issues so quickly. That’s easily a 10x win compared to a senior engineer doing the exact same debugging steps. IMHO that’s where the actual productivity boost is
Even without writing code LLMs are a huge help, analyzing code, doing code reviews, documenting code, etc... Even without writing a line of "code" LLM hugely speed up development and take away the annoying/boring work.
I honestly couldn't force myself to finish yet another blog post about how "we're not yet sure what impact LLMs will have on society" or whatever beleaguered point the author was attempting to make.
"Some random person's take on LLMs" was maybe interesting in 2024. Today it is not even remotely interesting.
There are a gazillion more interesting things happening today that ought to be of interest to the median HN reader. Can we talk about those instead?
I am an AI engineer and I honestly agree. Talking about LLMs feels like the new crypto, with some nuances (i.e. many innovative things being possible and done with LLMs whereas crypto innovations were… few and far between).
it’s felt like the new crypto to me for about 2-3 years now.
i was doing an ML Sec phd a year or two before all this hype took off. i took one of the OG transformer papers along to present at our official little phd reading group when the paper was only a few months old (the details of this might be a bit sketchy here, was years ago now).
now i want nothing to do with the field in any way shape or form. i’m just done.
the problem with this article is that he is right of course, but only right now. There is no reason to believe that future AI platforms won't be able to review code themselves and manage some aspects of themselves with minimal human oversight, yes we likely will always need a few experts
> There is no reason to believe that future AI platforms won't be able to review code themselves and manage some aspects of themselves with minimal human oversight
There are, IMHO, fewer reasons to believe they will be able to do that rather than not, though.
I have no stake in Fred Brooks. But No Silver Bullet seemed to be taken as gospel on this board. Sufficiently productivity-enhancing technology? Gimme a break man. Maybe you’ll get a 30% boost. Not a 10X boost.
> If its two empirical premises—that the accidental/essential distinction is real and that the accidental difficulty remaining today does not represent 90%+ of total—are true, then the conclusion which rules out an order-of-magnitude gain from reducing accidental difficulty follows automatically.
The article goes on to assume there’s no 10x gain to be had but misses one big truth.
Needing to type the code is an enormous source of accidental difficulty (typing speed, typos, whether you can be arsed to put your hands on the keyboard today…) and it is gone thanks to coding agents.
I think you're misunderstanding the paradigm shift completely -- AI does not just generate code N(x) more quickly. It thinks N(x) faster, it researches N(x) faster, it tests N(x) faster. There are hundreds of tasks that you'll find engineers are offloading to AI every day. The major hurdle right now is actually pivoting LLMs from just generating code: integrating those tasks into workflows. This is why tool-use and agentic workflows have taken engineering by storm.
Debugging, sanity checking, testing, etc. are the best uses of LLMs. Much better than writing code.
Developers should write their own code and use LLMs to design and verify. Better, faster architecture and planning, pre-cleaned PRs and no skill atrophy or loss of understanding on the part of the developer.
Not sure why you are downvoted but I agree. Additionally, perhaps LLMs are just like another higher programming language as the author said, and they still need someone to steer them.
I'm sure it was very difficult to program in machine code, but if now (or soon) anyone can just write software using a LLM without any sort of learning it changes everything. LLMs can plan and create something usable from simple instructions or ideas, and they will only get better.
I think LLMs will be (and already are) useful for many more things than programming anyway.
> I'm sure it was very difficult to program in machine code, but if now (or soon) anyone can just write software using a LLM without any sort of learning it changes everything. LLMs can plan and create something usable from simple instructions or ideas, and they will only get better.
Did you read the section "Power to the People?" ? In it, the author dismantles your thesis with powerful, highly plausible arguments.
> they will only get better.
I don't buy that's true. The "only" part, anyway. Look at how UX with software has evolved. This is gonna be an old man yells at clouds take, but before smartphones, there were hotkeys. And man, you could fly with those things. The computers running things weren't as fast as they are today, but you could mash in a a whole sequence thru muscle memory, and just wait for it to complete. Now, you have to poke at your phone, wait for it to respond, poke at it some more. It's really not great for getting fast at it. AI advancement is going to be like that. Directionally generally it will be better, but there's going to be some niche where, y'know what, ChatGPT-4o really had it in a way that 5.5 does not. (Rose colored glasses not included.)
True. Knowledge workers are cooked.
Claude connected to a Postgres (readonly obviously) and Datadog MCP servers in addition to access to the codebase can debug prod issues so quickly. That’s easily a 10x win compared to a senior engineer doing the exact same debugging steps. IMHO that’s where the actual productivity boost is
Even without writing code LLMs are a huge help, analyzing code, doing code reviews, documenting code, etc... Even without writing a line of "code" LLM hugely speed up development and take away the annoying/boring work.
A well researched and written piece
It was a welcome change to have a deliberate, well thought, and well-written article that tries to bring readers through a rational journey. Thank you
If you're interested in Fred Brooks's "No Silver Bullet," I also explored it in the context of LLMs: https://smartmic.bearblog.dev/no-ai-silver-bullet/
"LLM's Aren't Going to Fundamentally Change Software Development" Says Increasingly Nervous Man For Seventh Time This Year
Let's actually not talk about LLMs.
I honestly couldn't force myself to finish yet another blog post about how "we're not yet sure what impact LLMs will have on society" or whatever beleaguered point the author was attempting to make.
"Some random person's take on LLMs" was maybe interesting in 2024. Today it is not even remotely interesting.
There are a gazillion more interesting things happening today that ought to be of interest to the median HN reader. Can we talk about those instead?
I am an AI engineer and I honestly agree. Talking about LLMs feels like the new crypto, with some nuances (i.e. many innovative things being possible and done with LLMs whereas crypto innovations were… few and far between).
it’s felt like the new crypto to me for about 2-3 years now.
i was doing an ML Sec phd a year or two before all this hype took off. i took one of the OG transformer papers along to present at our official little phd reading group when the paper was only a few months old (the details of this might be a bit sketchy here, was years ago now).
now i want nothing to do with the field in any way shape or form. i’m just done.
Tedious LLM discourse isn’t aimed at AI engineers. It’s doomscrolling fodder for regular programmers.
the problem with this article is that he is right of course, but only right now. There is no reason to believe that future AI platforms won't be able to review code themselves and manage some aspects of themselves with minimal human oversight, yes we likely will always need a few experts
I'm reminded of this scene from the Matrix: https://www.youtube.com/watch?v=cD4nhYR-VRA where the older wise man discusses societies reliance on AI
"Nobody cares how it works, as long as it works"
We're done. I for one welcome our new AI Overlords, or more accurately still welcome the tech bro billionares who are pulling the strings
> There is no reason to believe that future AI platforms won't be able to review code themselves and manage some aspects of themselves with minimal human oversight
There are, IMHO, fewer reasons to believe they will be able to do that rather than not, though.
I have no stake in Fred Brooks. But No Silver Bullet seemed to be taken as gospel on this board. Sufficiently productivity-enhancing technology? Gimme a break man. Maybe you’ll get a 30% boost. Not a 10X boost.
Until recently. dramatic pause
And then AI happened.
> If its two empirical premises—that the accidental/essential distinction is real and that the accidental difficulty remaining today does not represent 90%+ of total—are true, then the conclusion which rules out an order-of-magnitude gain from reducing accidental difficulty follows automatically.
The article goes on to assume there’s no 10x gain to be had but misses one big truth.
Needing to type the code is an enormous source of accidental difficulty (typing speed, typos, whether you can be arsed to put your hands on the keyboard today…) and it is gone thanks to coding agents.